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Alma Mater Studiorum – Università di Bologna

DOTTORATO DI RICERCA IN

Oncologia e Patologia Sperimentale

Ciclo XXVII°

Settore Concorsuale di afferenza: 06/A2

Settore Scientifico disciplinare: MED/04

Cancer and aging: a multidisciplinary medicinal

chemistry approach on relevant biological targets

such as proteasome, sirtuins and interleukin 6

Presentata da: Marco Daniele Parenti

Coordinatore Dottorato Relatore

Prof. Pier Luigi Lollini Prof. Stefano Salvioli

Esame finale anno 2015

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Table of Content

Preface ............................................................................................................................................ 5

List of Abbreviations ...................................................................................................................... 7

1. Introduction ............................................................................................................................. 9

1.1 The Drug Discovery Process ............................................................................................. 9

1.2 Molecular Modeling and rational drug design in drug discovery ....................................... 9

1.2.1 Ligand based approaches ......................................................................................... 11

1.2.2 Structure based approach ......................................................................................... 12

1.3 Cancer and Aging in drug discovery ............................................................................... 12

1.4 Selected Targets and their Involvement in Human Pathologies ........................................ 13

2. Aim of the Thesis .................................................................................................................. 15

3. Proteasome and Immunoproteasome Inhibitors ...................................................................... 16

3.1 Proteasomes: mandatory terminators. .............................................................................. 16

3.2 Why is i-proteasome a potential therapeutic target? ......................................................... 18

3.3 I-proteasome as target for cancer therapy. ....................................................................... 19

3.3.1 I-proteasome in Multiple Myeloma .......................................................................... 20

3.3.2 I-proteasome in solid tumours. ................................................................................. 21

3.3.3 I-proteasome as a target for immunotherapy? ........................................................... 26

3.4 I-proteasome as target for neuropathologies .................................................................... 26

3.4.1 Alzheimer disease. ................................................................................................... 27

3.4.2 Multiple Sclerosis. ................................................................................................... 28

3.4.3 Temporal Lobe Epilepsy. ......................................................................................... 29

3.5 An overview of selective s- and i-proteasome inhibitors and enhancers. .......................... 30

3.6 Human immunoproteasome model. ................................................................................. 33

3.7 Computer-aided drug design approaches ......................................................................... 38

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3.8 Hit Identification ............................................................................................................. 39

3.9 Conclusion and perspective ............................................................................................. 46

3.10 Experimental Section................................................................................................... 47

4. Sirtuins in drug discovery ...................................................................................................... 48

4.1 Sirtuins: at the crossroad of metabolism, cancer, and inflammation ................................. 48

4.2 Sirtuins and cancer .......................................................................................................... 48

4.3 Sirtuin inhibitors ............................................................................................................. 50

4.4 Sirtuins activators ........................................................................................................... 52

5. Development of a Sirtuins selectivity model .......................................................................... 55

5.1 Sequence and structural comparison of the catalytic cores ............................................... 57

5.2 Structural comparison of the binding sites ....................................................................... 60

5.3 Structural superposition of available three-dimensional structures ................................... 61

5.4 Conclusions .................................................................................................................... 70

5.5 Experimental Procedures................................................................................................. 71

6. Discovery of new SIRT3 modulators ..................................................................................... 73

6.1 Targeting SIRT3 with structure-based drug design techniques ........................................ 74

6.2 Evaluation of acetylation pattern of mitochondrial proteins ............................................. 76

6.2.1 Evaluation of acetylation pattern of a specific protein target of SIRT3 ..................... 77

6.3 Evaluation of cell response to toxic stimulation ............................................................... 78

6.4 Conclusions .................................................................................................................... 79

6.5 Experimental Section ...................................................................................................... 79

7. Discovery of new SIRT6 inhibitors ....................................................................................... 81

7.1 Structure-based in silico screening .................................................................................. 82

7.2 Selectivity profiling ........................................................................................................ 85

7.3 Biological characterization of the identified SIRT6 inhibitors ......................................... 89

7.4 Conclusions .................................................................................................................... 92

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7.5 Experimental section ....................................................................................................... 93

8. Optimization of SIRT6 inhibitors - Quinazolinedione derivatives .......................................... 97

8.1 Selection of analog candidates for biological testing ....................................................... 97

8.2 Selectivity profiling ...................................................................................................... 102

8.3 Mechanism for SIRT6 inhibition by quinazolinediones ................................................. 103

8.4 Biological evaluation of quinazolinedione SIRT6 inhibitors .......................................... 104

8.5 Quinazolinedione inhibitors sensitize cancer cells to chemotherapeutics ....................... 107

8.6 Conclusions .................................................................................................................. 110

8.7 Experimental section ..................................................................................................... 111

9. Optimization of SIRT6 inhibitors – Salicylate derivatives ................................................... 116

10. Interleukin 6 inhibitors ..................................................................................................... 118

10.1 Biologic Functions of IL-6 ........................................................................................ 118

10.2 IL-6 Signaling Pathway ............................................................................................. 119

10.3 IL-6 in aging and aging-related diseases .................................................................... 120

10.4 IL-6 and cancer ......................................................................................................... 121

10.5 Pharmacological approaches to blockade of IL-6 signaling ........................................ 121

10.6 Drug repurposing....................................................................................................... 122

10.7 Screening for IL6 – IL6 receptor interaction modulators ............................................ 122

10.7.1 Screening of hexameric assembly IL-6/IL-6R/gp130 ligands .................................... 122

10.7.2 Screening of Protein-Protein Interface inhibitors ....................................................... 126

10.8 Conclusion and perspective ....................................................................................... 128

10.9 Experimental section ................................................................................................. 131

11. Concluding remarks and future perspectives ..................................................................... 132

Acknowledgements ..................................................................................................................... 133

References .................................................................................................................................. 134

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Preface

Publications and Patents

This thesis is based on the following publications and manuscripts:

Publications:

1. Parenti MD, Bruzzone S, Nencioni A, Del Rio A. Selectivity hot-spots of sirtuin catalytic

cores. To be Submitted to Structure

2. Sociali G, Galeno L, Parenti MD, Grozio A, Bauer I, Passalacqua M, Boero S, Donadini A,

Millo E, Bellotti M, Sturla L, Franceschi C, Ballestrero A, Poggi A, Bruzzone S, Nencioni

A, Del Rio A. Quinazolinedione SIRT6 inhibitors sensitize cancer cells to

chemotherapeutics. To be Submitted to ACS Chemical Biology

3. Parenti MD, Grozio A, Bauer I, Galeno L, Damonte P, Millo E, Sociali G, Franceschi C,

Ballestrero A, Bruzzone S, Del Rio A, Nencioni A. Discovery of novel and selective SIRT6

inhibitors. J Med Chem. 2014 Jun 12;57(11):4796-804.

4. Bellavista E, Andreoli F, Parenti MD, Martucci M, Santoro A, Salvioli S, Capri M, Baruzzi

A, Del Rio A, Franceschi C, Mishto M. Immunoproteasome in cancer and neuropathologies:

a new therapeutic target? Curr Pharm Des. 2013;19(4):702-18.

5. Bruzzone S, Parenti MD, Grozio A, Ballestrero A, Bauer I, Del Rio A, Nencioni A.

Rejuvenating sirtuins: the rise of a new family of cancer drug targets. Curr Pharm Des.

2013;19(4):614-23.

6. Andreoli F, Barbosa AJ, Parenti MD, Del Rio A. Modulation of epigenetic targets for

anticancer therapy: clinicopathological relevance, structural data and drug discovery

perspectives. Curr Pharm Des. 2013;19(4):578-613.

Patents:

WO/2014/170875. Quinazolinedione compounds with a sirtuin inhibiting activity.

Applicants: University of Genoa and Alma Mater Studiorum – University of Bologna.

Inventors: DEL RIO, Alberto; (IT), FRANCESCHI, Claudio; (IT), PARENTI, Marco,

Daniele; (IT), BAUER, Inga; (IT), BRUZZONE, Santina; (IT), GROZIO, Alessia; (IT),

NENCIONI, Alessio; (IT). Priority data: MI2013A000646 19.04.2013 IT.

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WO/2014/170873. Compounds with a sirtuin inhibiting activity. Applicants: University of

Genoa and Alma Mater Studiorum – University of Bologna. Inventors: DEL RIO, Alberto;

(IT), FRANCESCHI, Claudio; (IT), PARENTI, Marco, Daniele; (IT), BAUER, Inga; (IT),

BRUZZONE, Santina; (IT), GROZIO, Alessia; (IT), NENCIONI, Alessio; (IT). Priority

data: MI2013A000647 19.04.2013 IT.

Author’s contribution

The rational design and optimization of active molecules through molecular modeling techniques

was carried out by the candidate in the framework of the BioChemoInformatics Lab (Dept. of

Experimental, Diagnostic and Specialty Medicine, University of Bologna) while the biological

testing of compounds was conducted in collaboration with external laboratories.

Intellectual Property Issues

Chemical structures of sirtuin 3 modulators discussed in chapter 6 are considered for patenting,

therefore are not disclosed.

Salycilate derivatives of sirtuin 6 inhibitors discussed in chapter 8 are currently under biological

testing, and will be disclosed in a forthcoming publication.

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List of Abbreviations

2-NBDG = 2-[N-(7-nitrobenz-2-oxa-1,3-diazol-4-yl)amino]-2-deoxy-D-glucose

AD = Alzheimer‟s disease

AML = acute myeloid leukemia

APM = antigen-processing machinery

APP = amyloid precursor protein

C-like = caspase-like

CNS = central nervous system

CTL = CD8+ citotoxic T cell

CT-like = chymotrypsin-like

DUBs = deubiquitylating enzymes

EAE = autoimmune encephalomyelitis

ER = endoplasmic reticulum

ETC = electron transport chain

HD = Huntington‟s disease

HMGB1 = high-mobility group box-1

HTS = High troughput screening

IL = interleukin

IL-6 = Interleukin 6

IL-6R = Interleukin 6 receptor

ILR1 = interleukin receptor 1

i-proteasome = immunoproteasome

IS = immune system

MBP = myelin basic protein

MLR = mixed leukocyte reaction

MM = multiple myeloma

MS = multiple sclerosis

NAM = Nicotinamide

PBMC = peripheral blood mononuclear cells

PDB = Protein Data Bank

PHA = phytohemagglutinin

PHFs = tau-based paired helical filaments

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QSAR = quantitative structure-activity relationships

RMSD = root mean square deviation

SAR = Structure-Activity relationship

SBDD = structure-based drug design

s-proteasome = standard proteasome

STAC = sirtuin activating compounds

TcR = T cell receptor

TLE = temporal lobe epilepsies

T-like = trypsin-like

TLR-4 = Toll-like receptor-4

t-proteasome = thymus proteasome

T-reg = regulatory T cells

UPS = Ubiquitin Proteasome System

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1. Introduction

1.1 The Drug Discovery Process

Drug discovery is an extremely laborious and expensive process, requires an average of 13 years of

research and an investment of US$1.8 billion to bring a single drug from the bench to a patient's

bedside1; this is not in the least surprising, when one considers the complexity of biological

systems, most of which is only now beginning to be understood. Despite increase in investment in

drug discovery, the output is considerably low due to high rate of drug failure in clinical trials2.

Consequently, in order to reduce the cost and time of a drug to reach market, new technologies were

ventured. The genomics and the post-genomics eras, with the parallel advances in high-throughput

experimental methods and screening techniques to analyze whole genomes and proteomes, are

witnessing an explosion in the types and amount of information available, not only with respect to

the genome sequences and protein structures but also with respect to gene-expression, regulation

and protein–protein interactions. The availability of such information in publicly accessible

databases and the advances in both computing power as well as in computational methods for data

mining and modeling, have led to the emergence of several in silico approaches to systematically

address several questions in biology, with an obvious impact on drug discovery3,4

. Today, drug

discovery usually follows the general scheme presented in Figure 1.1. In short, the goal is to

identify a compound that can modulate the effect of a molecular target that regulates a biological

process related to a disease. Once a target has been identified and shown to be relevant in a disease

model, high throughput screening, or its theoretical counterpart, virtual screening, is usually

employed to generate a set of hit compounds. Following this, some of the promising molecules that

show good physico-chemical properties are subject to further chemical exploration. The synthesized

compounds in these lead series are evaluated and a structure–activity relationship (SAR) is derived,

as well as the pharmacokinetic and pharmacodynamic profiles of the most promising compounds.

The final phase in preclinical research is the transformation of a lead structure into a candidate drug,

which is then considered for testing in clinical trials.

1.2 Molecular Modeling and rational drug design in drug discovery

Historically, serendipity and trial and error have played a major role in the discovery of drugs. The

source of active substances has often been medical plants and herbs. With the advent of synthetic

organic chemistry and modern pharmacology a more systematic search for new pharmaceutically

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active compounds began, and these were often evaluated using animal experiments. The chemical

modification of lead compounds, on a trial-and-error basis, typically led to compounds with

improved potency, selectivity and bioavailability and reduced toxicity. However, this approach is

costly and labor- and time-intensive and researchers in the pharmaceutical industry are constantly

developing methods with a view to increase the efficiency of the drug discovery process. Two

directions have evolved from these efforts. The „random‟ approach involves the development of

HTS assays and the testing of a large number of compounds, and combinatorial chemistry is used to

satisfy the need for extensive compound libraries. The „rational‟, approach relies on the knowledge

of the structure of the target protein or knowledge about available potential compounds. Rational

design approach involves the prediction of hypothetical ligands for the target protein from

molecular modeling and the subsequent chemical synthesis and biological testing of specific

compounds. Many rational design approaches have been suggested to increase the cost-

effectiveness of discovery programs. Such approaches include ligand based approaches such as

pharmacophore modeling, determination of quantitative structure-activity relationships (QSAR),

which use accumulated information for ligands of previously executed discovery programs, and

receptor based approaches such as docking, which use available information about target protein

structure. One of most commonly used rational design approach is the so-called virtual or in silico

screening; this methodology involves the computational filtering of a large body of molecules (e.g.,

those comprising a corporate database or a database of commercially available molecules) to

identify those that have a high probability of activity in the biological test system of interest. Thus a

virtual screening method takes as input all those molecules that might be acquired (or synthesized)

and tested, and then outputs those few that should be tested. Although there are numerous methods

for performing a virtual screening, they can be roughly classified into two main types: ligand-based

approaches which do not utilize the structure of the biological target in screening, and structure-

based approaches, which utilize the structure of the biological target, usually obtained by NMR or

X-ray methods, and a variety of molecular docking algorithms and scoring functions. Hybrid

approaches which combine aspects from ligand-based and structure-based methods are also

frequently employed in virtual screening studies.

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Figure 1.1 Drug Discovery Pipeline.

1.2.1 Ligand based approaches

Ligand-based approaches typically utilize knowledge of a set of compounds with known activity

against the biological target. These approaches are frequently employed in the absence of structural

information on the target in question. The key concept in ligand based approaches is that

compounds that are structurally similar or have similar structural components to the known active

compounds are more likely to have activity themselves. A variety of ligand-based screening

methods have been developed, such as substructure and similarity searching, pharmacophore

searching, clustering methods, and QSAR methods. Among these, the pharmacophore screening is

certainly one of the most used methods.

Pharmacophore modeling allows determining the spatial arrangement of chemical features that

confer drug activity toward a target receptor. Having established the chemical space occupied by

active ligands, pharmacophore modeling software allows researchers to create 3D structure-activity

relationships, screen databases, and generate hits without the benefit of a receptor structure.

Compared to other ligand-based methods provides several advantages: i) can take into account 3D

conformational variations and functional group properties such as polarity, hydrogen bond

potential, aromaticity, and hydrophobicity; ii) can identify lead compounds that are structurally

dissimilar to those already known, a process known as „scaffold-hopping‟ or „lead-hopping‟; iii) can

also incorporate known structural information of the biological target‟s active site, if any is known,

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in the form of exclusion spheres or a molecular surface, both of which create barriers the

compounds being searched are not permitted to encroach.

1.2.2 Structure based approach

In structure-based drug design (SBDD), the knowledge of the three-dimensional structure of a target

is exploited to design small molecules able to tightly bind the active site and modulate the

biological function in a desired way; the same tools could be used to assess the selectivity of the

potential ligands towards different targets, making it easier to develop drugs with fewer side effects.

This could reduce the time and resources needed to identify new interesting lead compounds. The

structure-based methods, such as docking, have been successfully used to identify new hits from

large libraries of chemical compounds and to predict their binding modes and affinities, and

currently represent one of the primary methodologies used to discover new “hit” compounds.

Molecular docking involves two main processes: pose prediction and scoring. In pose prediction a

search algorithm determines an optimal conformation and orientation for a given compound in the

receptor, or active site. This is followed by scoring to determine whether the pose will be accepted

or rejected. Therefore, docking techniques enable both to predict the binding mode of a ligand and

to roughly estimate its binding affinity for the biological target. Several scoring functions have been

developed to approximate the interaction energy between proteins and related ligands, and all of

these are simple linear mathematical models that estimate chemical properties (such as shape,

charge distribution, hydrophobic/hydrophilic potentials and so on) of the molecules.

1.3 Cancer and Aging in drug discovery

The ultimate goal of biomedical research is to translate laboratory discoveries or clinical

observations into new therapies to ameliorate disease and extend life expectancy and quality,

namely the average total number of years of remaining meaningful life at a given age5. Ageing is a

complex and multifactorial process characterized by the many forms of damage accumulation at the

molecular, cellular, and tissue level that progress with advancing age, decreasing the body‟s normal

response and function. No single theory currently exists that can explain all of the hallmarks of

ageing, suggesting that ageing is a multi-step and multi-event process6. At first glance, cancer and

ageing have an inverse relationship because cancer cells are capable of uncontrolled growth and

division, whereas ageing cells have a diminished proliferative capacity. Indeed, it has been well-

established that older adults have a higher risk for cancer, as well as a higher risk of the onset of

others chronic inflammation-associated diseases such as diabetes, stroke, and neurodegenerative

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disease. Ageing is involved in a number of events responsible for carcinogenesis and cancer

development at the molecular, cellular, and tissue levels. Actually, ageing and cancer have common

origins due to internal and environmental stress and share some common hallmarks such as

genomic instability, epigenetic alteration, aberrant telomeres, inflammation and immune injury,

reprogrammed metabolism, and impaired degradation of intracellular biomolecules and organelles7.

The pharmaceutical industry is always searching for new biological targets to generate novel

therapies; aging could represent a “blockbuster” market because the target patient group includes

potentially every person, and humans are very willing to pay for chronic medical therapy in order to

delay the aging process. Thus, there are many convincing reasons why aging and aging-related

diseases should be a major focus for drug discovery8. At the same time, oncology has become the

largest therapeutic area in the pharmaceutical industry in terms of the number of projects, clinical

trials and research and development (R&D) spending9, but despite the enormous resources being

invested in prevention and treatment, cancer remains one of the leading causes of mortality

worldwide.

1.4 Selected Targets and their Involvement in Human Pathologies

Among all possible protein targets involved in aging-related diseases and cancer, we focused our

attention on proteasome (and its variant immunoproteasome), sirtuins and interleukin 6. These three

targets are completely unrelated and play different roles in human cells, but the modulation of its

activity (activation or inhibition) using small molecules could have beneficial effects on one or

more aging-related diseases and cancer.

Proteasome is the central catalytic unit of the ubiquitin proteasome system (UPS), which is used to

degrade the main part of intracellular proteins. 20S standard proteasome (s-proteasome) is a

cylinder-shaped complex that is composed of four stacked rings, each consisting of seven protein

subunits; while associated with several regulator complexes, performs two crucial functions for cell

metabolism: first of all, by degrading obsolete, misfolded or aberrant proteins proteasomes perform

housekeeping function and maintain the cellular homeostasis; secondly, through the time-specific

cleavage of short-life proteins, like transcription factors or transcription factor‟s inhibitors, are able

to switch on/off many cellular pathways. Hence, the proteasome as central core of the UPS, is a sort

of mandatory terminator of proteins and its inactivation leads to cellular death by apoptosis or

necrosis. The immunoproteasome (i-proteasome) originates from the substitution of some

constitutive catalytic subunits stimulated by pro-inflammatory citokines such as INF-γ e TNF-α,

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and it is highly expressed in normal conditions only by specific cells types of the human body, such

as those involved in immune-related function. Inhibiting or enhancing activity of proteasome or

immunoproteasome could represent a promising strategy to counteract neurodegenerative diseases

as well as cancer pathologies.

Sirtuins are a family of NAD+-dependent enzymes that was proposed to control organismal life span

about a decade ago. While such role of sirtuins is now debated, mounting evidence involves these

enzymes in numerous physiological processes and disease conditions, including metabolism,

nutritional behavior, circadian rhythm, but also inflammation and cancer. In mammals, seven

sirtuins have been identified (SIRT1-7), of which two are predominantly nuclear, SIRT6 and

SIRT7, two are nuclear and cytosolic, SIRT1 and SIRT2, and three are mitochondrial, SIRT3-5.

Sirtuin activators could slow the process of cellular senescence, and therefore could be useful in

treatment of metabolic and neurodegenerative diseases, while sirtuin inhibitors could be appealing

for the development of new anticancer and anti-inflammatory therapies.

Interleukin-6 (IL-6) is a pleiotropic cytokine with significant functions in the regulation of the

immune system, and plays a pivotal role in host defense against pathogens and acute stress.

However, increased or deregulated expression of IL-6 significantly contributes to the pathogenesis

of various human diseases. The pathological roles of the IL-6 pathway in inflammation,

autoimmunity, and cancer were revealed by numerous preclinical and clinical studies. Therapeutic

strategies targeting the IL-6 pathway are in development for cancers, inflammatory and

autoimmune diseases.

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2. Aim of the Thesis

The overall aim of the work presented in this thesis was the rational design of new compounds able

to modulate activity of relevant targets involved in cancer and aging-related pathologies, namely

proteasome and immunoproteasome, sirtuins and interleukin 6.

The objective of the thesis was accomplished through a multidisciplinary approach that involved

different steps:

Step 1: Hit identification

State-of-the-art molecular modeling techniques, mainly virtual screening methods, was applied to

selected targets to identify a limited number of small molecules able to modulate their biological

activity

Step 2: In Vitro Testing

Compounds identified during Step 1 were submitted to biological testing in vitro to measure

biological activity and identify the structure-activity relationships (SAR) that allow understanding

the minimum requirements for activation or inhibition of biological targets.

Step 3: Lead Optimization

The more promising chemical scaffolds and the SAR data coming from Step 2 were used to design

specific structural modifications, obtained trough chemical synthesis, by introducing and modifying

functional groups able to improve biological activity and, at the same time, to affect

pharmacokinetic and pharmacodynamic profiles, such as selectivity towards similar targets or

bioavailability.

Step 4: Biological Profiling

Lead compound obtained from Step 3 were submitted to complete biological profiling to verify the

agreement between measured activation or inhibition of each compounds and its functional activity

in selected tissues.

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3. Proteasome and Immunoproteasome Inhibitors

3.1 Proteasomes: mandatory terminators.

Proteasome is the central catalytic unit of the ubiquitin proteasome system (UPS), which is used to

degrade the main part of intracellular proteins. 20S standard proteasome (s-proteasome) is a

cylinder-shaped complex that is composed of four stacked rings, each consisting of seven protein

subunits. Each of the two inner rings contain β subunits (β1 - β7), three of which (β1, β2, β5) harbor

the proteolytic active sites catalyzing, by their N-terminal threonine residues, a caspase-like (C-

like), trypsin-like (T-like) and chymotrypsin-like (CT-like) activity, respectively. The α-subunits

(α1 - α7), which compose the two outer rings, have other functions such as gating the central

chamber (thereby enabling the entry of substrates into the inner proteolytic cavity) and the binding

of regulator complexes like the PA700, PA28 and PA20010,11

. The association of these regulator

complexes leads to formation of multiple forms of proteasomes like 26S (PA700-20S) and 30S

(PA700-20S PA700), PA28-20S and PA28-20S-PA28 complexes as well as hybrid proteasomes

(PA28-20S-PA700). 26S/30S proteasomes recognize target proteins by the presence of

polyubiquitin chains which are then released and the target proteins are unfolded and cleaved in a

ATP-dependent manner12–14

. The covalent attachment of ubiquitin to acceptor lysines in a substrate

is a multi-step process that begins with activation of ubiquitin by E1 enzyme, which transports

ubiquitin to an ubiquitin-conjugating enzyme (E2). The latter transfers ubiquitin to substrate either

by itself or in cooperation with an ubiquitin ligase (E3). Afterwards, additional ubiquitins can be

added to the first, by linkage to one of its lysines, giving rise to the polyubiquitin chain. After

proteasome targeting, ubiquitin is recycled by deubiquitylating enzymes (DUBs), some of which

also function to oppose the action of E3s15

. It is worthy to note that the binding of

polyubiquitylated proteins to the 19S regulator of mammalian and yeast 26S proteasomes enhances

the peptidase activities of 20S proteasome about two-fold in a process requiring ATP hydrolysis16

.

UPS performs two crucial functions for cell metabolism: first of all, by degrading obsolete,

misfolded or aberrant proteins proteasomes perform housekeeping function and maintain the

cellular homeostasis; secondly, through the time-specific cleavage of short-life proteins, like

transcription factors or transcription factor‟s inhibitors (e.g. UPS cleaves IκB-α leading to the

entrance of NF-κB in the nucleus) proteasomes are able to switch on/off many cellular pathways.

Hence, the proteasome as central core of the UPS, is a sort of mandatory terminator of proteins and

its inactivation leads to cellular death by apoptosis or necrosis17–19

.

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Proteasome not only cleaves proteins but also can ligate two of the produced fragments, thereby

generating peptides with a sequence that differs from the sequence of the original substrate. This

process, also known as proteasomal splicing, has been demonstrated in vivo so far only for four

MHC class I-restricted epitopes20–24

, leading to the assumption that proteasomal splicing activity is

a rare event, although recent results obtained in vitro suggest that it is in fact part of the normal

activity of proteasomes25

. Because of some inherent technical difficulties and the unexpected

novelty of proteasomal splicing, the biochemical models as well as the understanding of the

relevance of proteasomal splicing activity were so far only partially investigated. A deeper study of

this process would have also implications from the immunological point of view, taking into

account that PCPS highly increases the antigenic diversity26

. Indeed, proteasomes are not only

responsible for the degradation of the greater part of the cytoplasmic proteins but they also generate

the vast majority of virus- or self-derived peptides presented by the MHC class I molecules on cell

surface27,28

. This latter function is generally aided by the interferon-γ (IFN-γ)-induced synthesis of

the PA28-α and PA28-β proteasome activator subunits as well as of the β1i, β2i, β5i alternative

catalytic subunits (also known as LMP2, MECL-1 and LMP7, respectively) with concomitant

formation of the immunoproteasome (i-proteasome). The different catalytic subunits confer to i-

proteasome differences in cleavage preferences and degradation rates, which however, vary from

substrates to substrates29

.

Recently, it has been described a pivotal involvement of i-proteasome in cytokine-mediated

inflammation in mice, because its depletion altered T cell receptor (TcR) repertoire formation, the

number of CD8+ T cells in the spleen, T cell survival and early activation, differentiation into

inflammatory effector cells, release of pro-inflammatory cytokines, such as IL-6 which plays a

pivotal role also in cancer (see below), as well as response to oxidative stress30–33

.

Although the removal of oxidized proteins by proteasomes is clearly established34–36

, recent works

suggest that i-proteasomes are more prone than s-proteasome at eliminating them. Indeed, blocking

expression of β1i by siRNA significantly reduces the adaptive response to mild oxidative stress in

MEF cell lines37

whereas β1i -/- mice exhibit higher levels of protein carbonyls in brain and liver

upon aging than those of their wild-type littermates38

. In addition, Seifert and co-workers showed

that i-proteasome is a key element for the clearance of oxidized proteins and aggresome-like

induced structures upon INF-γ stimuli39

. These independent observations, gathered by exploiting

new i-proteasome specific inhibitors as well as i-proteasome knock-out (KO) mice, opened de facto

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a new era in the investigation of i-proteasome functions, which were so far almost merely confined

to antigen presentation.

It is worth to mention that between s- and i-proteasomes a group of intermediate-type proteasomes

does exist, which are characterized by different, and tissue-specific, combinations of standard and

inducible catalytic subunits, and probably by different post-translational modifications, able to alter

their proteolytic activity, sensitivity to inhibitors and outer surface charge40–42

. At last, but not least,

the group of Tanaka described another isoform of β5 subunit (β5t) specific for thymus, which

characterizes the so called thymus proteasome (t-proteasome), the third main isoform of

proteasomes, having specific functions in the positive/negative selection of thymocyte and in CD8+

T cell development43,44

.

3.2 Why is i-proteasome a potential therapeutic target?

The research for specific modulators of the i-proteasomes activity is a very hot topic of today‟s

biology for several reasons. The first one, and likely most relevant, is that i-proteasome is highly

expressed in normal conditions only by specific cells types of the human body, such as those

involved in immune-related function or few organs like the liver45

, whereas the majority of cells

barely have i-proteasome. The most famous inducer of i-proteasome synthesis is IFN- , usually

secreted by cells during inflammation, although different studies suggested also other mechanisms,

such as the activation of Toll-like receptor-4 (TLR-4)46,47

. In pathological situation like

neurodegenerative diseases, inflammatory processes induce i-proteasome synthesis in cells (e.g. in

neurons) where normally i-proteasomes are absent48–51

. Therefore, this disease-related expression of

i-proteasome becomes a potential marker of pathological processes (potentially exerting both

beneficial and detrimental effects) and possibly a therapeutic target.

Studies carried out on animal models showed that the inhibition or the absence (in 5i KO mice, for

example) of i-proteasomes could either ameliorate or worsen the course of the disease in a disease-

specific manner31,32,52

. Although the specific enhancement of the i-proteasome activity could likely

be an appealing strategy for future therapies of selected diseases, the research in this direction is

dampen by the absence of effective and treatment-compatible i-proteasome enhancers. Therefore,

investigations on this topic are at present carried out only with i-proteasome inhibitors. These

studies highlight two issues that must be carefully considered: the specificity of the inhibitors for

the i-proteasome subunits and the potential presence of compensatory mechanisms activated in i-

proteasome KO mice. The first issue will be discussed in next paragraph. The latter issue, on the

19

contrary, has been raised by Muchamuel et al. by suggesting that the increased amounts of 5 and

decreased 1i and 2i subunits in 5i KO mice compared to wild type mice may mask the 5i-

specific functions in complex cellular processes such as inflammatory responses31

.

3.3 I-proteasome as target for cancer therapy.

Cancer development is a multifactorial process, which involves various genetic alterations including

the activation of oncogenes, inactivation of tumour-suppressor genes, disregulation of cell cycle

progression and apoptosis, as well as modification of immunosurveillance53

. Because of the crucial

role of proteasomes in controlling many of these biological and metabolic processes as well as the

production of MHC class I-restricted epitopes responsible for CD8+ citotoxic T cell (CTL)

activation54

, this protease has become an attractive target for the treatment of malignancies55–58

(Table 3.1). A number of preclinical studies showed that tumour cells are often more sensitive to

proteasome blockade than normal cells and different mechanisms have been hypothesized59

. For

example, many types of malignant cells rapidly proliferate and might accumulate defective proteins

at a much higher rate than normal cells, thereby increasing their dependency on proteasome as

disposal mechanism58

. Moreover, inhibition of proteasome causes an inactivation of NF- B

pathway, which is involved in maintaining drug or radiation resistance in cancer cells, and it might

reverse or bypass some alterations of cell-cycle and apoptotic checkpoint that lead to

tumourigenesis59

. In addition, while the activity of s-proteasome is generally found to be up

regulated in cancer cells60

, the levels of i-proteasome seem to vary depending on tumour types. In

particular, i-proteasome is induced in haematological malignances, such as Multiple Myeloma

(MM); therefore, the selective inhibition of this proteasome isoform may increase the effectiveness

of proteasome blocking and alleviate side effects associated to non selective inhibitors of specific

proteasome isoforms such as bortezomib, carfilzomib and NPI-005261

(Figure 3.1 and Table 3.2). In

accordance, first-generation i-proteasome inhibitors have been developed and tested in vitro and in

animal model trials. Moreover, considering the involvement of proteasomes in MHC class I-

restricted epitope production and the different contribution of s-proteasome, i-proteasome as well as

intermediate type proteasomes to process specific antigens, the selective inhibition of these different

isoforms could play a crucial role in CTL-based immunotherapy, as described below62

.

20

Disease family Disease

Type of i-

proteasome

modulation

Used

inhibitor

Tumours MM Inhibition PR-92463

MM Inhibition IPSI-00161

Prostatic cancer Inhibition UK-10164,65

Table 3.1. Pathologies where an i-proteasome specific inhibition as therapy has been tested. The list includes the main

pathologies where an i-proteasome inhibition has been tested. MM=Multiple Myeloma.

3.3.1 I-proteasome in Multiple Myeloma

MM is a neoplastic plasma-cell disorder characterized by clonal proliferation of malignant plasma

cells in the bone marrow microenvironment, accounting for approximately 1% of neoplastic

diseases and 13% of haematologic cancers. Myeloma arises from an asymptomatic premalignant

proliferation of monoclonal plasma cells derived from post-germinal-center B cells; multistep

genetic and microenvironmental changes lead to the transformation into a malignant neoplasm. In

particular, primary early chromosomal translocation occurs at the immunoglobulin switch region,

while subsequent rearrangements, gene mutations and epigenetic dysregulation have been reported

to affect disease progression, by altering the expression of several genes including adhesion

molecules as well as responses to growth stimuli in the microenvironment. Interaction between

myeloma cells and bone marrow or extracellular matrix increases tumour growth, survival and drug

resistance, through the production of cytokines and growth factors such as IL-6, IL-10 and vascular

endothelial growth factor among others66

. At present, the anti-myeloma therapeutic regimes are

based on the different combination of immunomodulatory drugs (e.g Dexamethasome,

Lenalidomide, Prednisone) and the proteasome inhibitor bortezomib, thereby leading to the

disruption of several signaling pathway67

. In particular, proteasome inhibition stimulate multiple

apoptotic pathways, including the induction of endoplasmic reticulum (ER) stress response, and by

inhibiting the NF- B pathway, it down-regulates the productions of angiogenesis factors, cytokines

such as IL-6 and cell adhesion molecules in the microenvironment58,66

. However, considering the

adverse effects related to the generalized proteasome inhibition mediated by bortezomib (e.g.

haematological toxicity and peripheral neuropathy) and the up-regulation of i-proteasome in

primary MM patient (CD138+) tumour cells

63, a number of first-generation i-proteasome inhibitors

have been tested in vitro and in animal models, showing anti-myeloma activity, mediated by several

mechanisms. The selective inhibition of β5i subunit (e.g by PR-924 or PR-957, see below) blocks

21

growth and triggers apoptosis in MM cell lines and MM patient‟s primary cells, without affecting

normal peripheral blood mononuclear cells. Moreover, it allows to overcome bone marrow stromal

cells mediated drug resistances, even in presence of IL-6, which is a pro-survival factor not only for

for MM but also for other kind of tumours, such as breast cancer68

. Additionally, it inhibits tumour

growth in both human plasmacytoma xenograft and SCID-hu mouse model, by decreasing the

levels of IL-6, increasing apoptosis and inhibiting angiogenesis63

. Noteworthy, an inhibition of the

presentation of tumour epitopes that are β5i-dependent has also been reported31

. The same anti-

proliferative activity has been showed in vitro by inhibiting the 1i subunit (e.g by IPSI-001, see

below) in lymphoid-derived tumour cells lines and MM patient-derived samples. This effect is

exerted by the combined activation of the intrinsic and extrinsic apoptotic pathway and the

inhibition of NF-κB signaling. Moreover, an overcome drug resistance to doxorubicin, melphalan

and bortezomib as well as an improved toxicity profile in nonhematopoietic tissues have been

described61

.

3.3.2 I-proteasome in solid tumours.

At present, no data are available on the application of i-proteasome specific inhibitors to other kind

of haematological malignancies such as acute myeloid leukemia (AML) and solid tumors, although

the selective inhibition of the 1i subunit (i.e. by UK-101, see below) led to growth-inhibitory

activity in prostatic cancer cells64,65

. In these tumours, a heterogeneus expression of the antigen-

processing machinery (APM), including i-proteasome subunits, is observed and often correlates to

the progression of disease and the immune response escape53

. Indeed, in bone marrow biopsies of

AML patients multiple defects in APM expression were reported and remarkably a progressive

downregulation of APM was seen from initial diagnosis to relapse69

. In renal carcinoma cells lower

levels of 1i and 5i subunits as well as of the epitope transporters into ER (i.e. TAPs) have been

found and they were more pronounced in metastatic lesions than primary tumour53

. The same

scenario has been observed in hepatocellular carcinoma cell lines70

and primary malignant

melanoma lesions, associated with lack of spontaneous regression71

whereas mice lacking 1i

subunit develop spontaneous uterine leiomyosarcoma72

. An up-regulation of i-proteasome and an

increased CTL response against tumour antigens have been observed in vitro in hepatocarcinoma

cell lines after the administration of INF-70

, suggesting that in certain cases restoration rather than

inhibition of i-proteasome functionality could be effective in anti-tumour induced-response, by

enhancing the production of tumour-specific MHC class I-restricted epitopes.

24

Figure 3.1. Chemical structures of promiscuous proteasome inhibitors.

25

Structure Selectivity Inhibition

mechanism Subunit Activity

1 All All reversible

2 β5, β5i CT-like irreversible

3 β2, β5, β5i, β2i CT-like>T-like>C-like irreversible


5 β5, β1i CT-like reversible

6 All All irreversible

7 β1, β1i C-like irreversible

8 β2 T-like irreversible


10 β5 CT-like reversible

11 β1, β2, β5 CT-like>T-like>C-like irreversible


13 β5 CT-like>T-like, C-like reversible

14 β2>β5>β1 T-like>CT-like>C-like irreversible

15 β2>β5>β1 T-like>CT-like>C-like irreversible

16 β5>β1, β2 CT-like>C-like, T-like irreversible

17 β5>β1, β2 CT-like>C-like, T-like irreversible

18 All All irreversible

19 β5 and β2 CT-like>T-like irreversible

20 β5>β1,β2 CT-like>C-like, T-like reversible


22 β5 CT-like non covalent


24 β5>β2, β1 CT-like> T-like, C-like non covalent

25 β5 CT-like

26 β5 CT-like

27 β1 / β5 C-like / CT-like irreversible

28 β5 CT-like non covalent

Table 3.2. Activity profiles of promiscuous inhibitors of s- and i-proteasomes. Structure enumeration of proteasome

inhibitors refers to what reported in Fig. 3.1. Proteasome activities, as defined with short-fluorogenic substrate assay,

are shortened as following: CT-like = chymotrypsin-like, T-like = trypsin-like, C-like = caspase-like.

26

3.3.3 I-proteasome as a target for immunotherapy?

The concept of using vaccination in the treatment of cancer has been tied to the history of vaccines

themselves. However, the major interest in it has recently grown with the increasing understanding

of the role that the immune system (IS) plays in shaping the biological behavior of cancer, including

the identification of tumour antigens recognized by CTLs73

. As above mentioned, the MHC class I-

restricted antigen presentation is generally enhanced after IFN- stimuli by inducing the expression

of i-proteasomes, PA28- , TAPs and MHC class I molecules29

. It has been proposed that i-

proteasome increases the production of MHC class I-restricted epitopes because of its higher

inclination for generating peptides with hydrophobic and basic C-termini, which shall have a better

affinity for the APM74

. Nevertheless, the group of Van den Eynde reported some examples of MHC

class I-restricted tumour (self)-epitopes that were better generated by s-proteasome than i-

proteasome75–78

. They speculated that s-proteasome could better generate self-epitopes in contrast to

i-proteasome more efficient in viral epitope production75

. This difference between s- and i-

proteasome might have fallouts on CD8+ T cell-mediated immune response at different levels,

including thymocytes development, tolerance induction, CTL activation as well as cancer

immunotherapy. Although this theory is quite appealing, we believe that the limited number of self-

and viral-epitopes properly investigated so far does not allow to statistically confirm it. In addition,

this theory recently evolved because the group of Van den Eynde showed also that two tumour

epitopes of MAGE-A*03 and –A*10 proteins are processed exclusively by intermediate type

proteasomes, which are variabily expressed in tumour, dendritic cells and in normal tissues18,79,80

.

In summary, the results obtained in the last decade suggest that the repertoire of antigens presented

by a given cell is strongly affected by its proteasome content, composition as well as the levels of

their regulators, which could modify cleavage properties of protein substrates81

. This observation

could have strong implications on cancer immunosurveillance although further investigations are

mandatory to define the role of different protesome isoforms in immunotherapy against cancer.

3.4 I-proteasome as target for neuropathologies

Although in young healthy human central nervous system (CNS) i-proteasomes are almost absent,

they have been detected in cells of different CNS areas from elderly subjects as well as from

patients affected by Alzheimer (AD) or Huntington (HD) diseases49,51

, Multiple Sclerosis (MS) with

a concomitant expression of the PA28- complex50

and very recently in Temporal Lobe Epilepsies

(TLE)48

(Table 3.1). The induction of i-proteasome and PA28- expression in brain could have

27

effects on different pathways because UPS regulates in neurons, for example, the pre- and post-

synaptic plasticity and the protein turnover82

. Processes and mechanisms responsible of cerebral i-

proteasome synthesis are still unknown. Neuroinflammation could be the trigger of i-proteasome

expression as we hypothesized for aging, AD, MS and TLE48–50

, which will be briefly here

discussed as example of age-related (i.e. AD) or autoimmune (i.e. MS) neurodegenerative diseases

and epilepsies (i.e. TLE). It is worthy to note that i-proteasome synthesis induction could be limited

to CNS or be the result of a phenomenon started in periphery and transferred to CNS, as it might

occur during the onset of MS. Accordingly, IS regulation in other organs could have implications

also at CNS level, including the regulation of i-proteasome expression. For example, a cross-talk

between brain and gut with implications for the IS has been proposed recently in studies on

experimental autoimmune encephalomyelitis (EAE)83–85

.

3.4.1 Alzheimer disease.

AD is a devastating neurodegenerative disorder of the CNS occurring most frequently in later stages

of adulthood. AD is associated with a specific pattern of pathological changes in brain that result in

neurodegeneration and progressive development of dementia. These pathological hallmarks of AD

are neuronal loss accompanied by intraneuronal neurofibrillary tangles formed of tau-based paired

helical filaments (PHFs) and extracellular senile plaques of -amyloid86

. It has been reported that

PHFs inhibit proteasome activity and it has been suggested that this inhibition may induce neuronal

damage in AD87

. Furthermore, UPS is also involved in the control of the physiological maturation

of the -amyloid precursor protein by modulating the intracellular concentration of presenilins88

.

An inhibition of proteasome activity in crude extracts from AD affected brain areas has been

described, whereas an investigation carried out on 20S proteasome purified from the frontal

neocortex of AD patients suggested that the observed drop of 20S proteasome activity in AD tissue

could be due to the presence of inhibitory molecules more than to an intrinsic decrease of the 20S

proteasome functionality87,89,90

.

AD occurs usually in elderly brain, where i-proteasome expression is already present in different

cells types, maybe as a result of the inflamm-aging phenomenon and/or as attempt to cope with the

increasing oxidative stress91

. I-proteasome expression is further induced in hippocampi (but not in

cerebellum) of AD patients49

and its presence varies among different neuronal sub-types. It has

been speculated that i-proteasome expression might be an attempt to tackle the accumulation of

oxidised proteins and phosphorylated tau that occur during AD progression, since they are both

preferentially degraded by i-proteasome39,92

. However, the few available information about the role

28

of i-proteasome in AD does not allow us to estimate if an inhibition of i-proteasome function would

lead to a progression or a reduction of the neurological damage. In addition, AD clinical symptoms

emerge when the neurological damage is already pronounced; therefore, we might speculate that a

modulation of i-proteasome function at the early stages of the disease could have different effects

(even opposite) than a modulation in the late stages of AD.

3.4.2 Multiple Sclerosis.

MS is the most common autoimmune disorder of the CNS. It is characterized by multifocal areas of

demyelization (plaques), chronic inflammation and damage to oligodendrocytes and neurons. The

cause of MS is still unknown and disease pathways are poorly understood. However, the association

of HLA-DRB1*15 and other HLA class I (e.g. HLA-A*02 and HLA-A*03) and class II alleles, the

presence of autoreactive T lymphocytes together with other inflammatory cells and cytokines in

active MS lesions suggest an autoimmune pathogenesis. Accordingly, EAE, a classical mouse

model for MS, can be induced by the administration of myelin antigens or CD4+ and CD8

+ T

lymphocytes specific for those antigens93–95

. It has been proposed that the first bout of the disease is

mediated by CD8+ T cells while the first relapse and MS progression are mediated by CD4

+ T cells

through different mechanisms such as antigen release and epitope spreading96

.

Although preliminary observations on white and grey matters of MS patients suggested that 20S

and 26S proteasomes activity is decreased compared to controls97

, no information are so far

available on proteasome activity in plaques, although an accumulation of i-proteasome has been

observed50

. In CNS of subjects affected by MS, i-proteasomes are expressed in different cell types

such as oligodendrocytes, astrocytes, macrophages/microglia, infiltrating lymphocytes and, weakly,

in neurons as demonstrated by double IHC assays. A similar expression has been described also for

the subunit of the PA28 complex. Intriguingly, the polymorphic variant HH at the codon 60 of

1i subunit was significantly less present in a sizeable Italian MS population (OR = 0.44) restricted

to females carrying the HLA-A*02 allele50

. The Authors correlated this genetic data to the

observation that a HLA-A*02-restricted epitope (MBP111-119), which activates memory T cells

preferentially in blood of MS patients98–100

, was produced in lower amount during in vitro digestion

by 20S i-proteasome carrying the variant HH at the codon 60 of 1i subunit. Authors speculated

that a lower production and presentation of this epitope as well as other myelin epitopes bound to

the HLA-A*02, could reduce the probability to disrupt the physiological tolerance (central or

peripheral) of myelin-specific CTLs and/or their cytotoxicity towards oligodendrocytes, thereby

restraining the MS onset50

. The i-proteasome activity and the 1i R60H polymorphism might have

29

implications also at thymic level during the selection of myelin –reactive thymocytes although no

exhaustive information is available to this regard.

Furthermore, the study of Seifert and colleagues suggested another i-proteasome-related mechanism

that could affect the MS onset. Indeed, they showed that i-proteasome is essential for an efficient

clearance of oxidised and polyubiquitylated proteins upon IFN- induced oxidative stress, thereby

preserving protein homeostasis during inflammation. Accordingly to this observation, they reported

that 5i ko mice showed an earlier onset and worst clinical score than wild type mice in an EAE

model39

. Although these two studies would suggest that i-proteasomes may influence onset and

progression of MS affecting both the myelin-specific CD8+ T cell activity and the response of

different cell types to the inflammatory aggression both in periphery and in the CNS, further

independent confirmations are needed before drawing a model connecting i-proteasome and MS

which could have a remarkable spin-off for future therapeutic approaches to the disease.

3.4.3 Temporal Lobe Epilepsy.

Epilepsy is a neurological disorder that affects about 50 million people worldwide and is

characterized by an enduring predisposition to generate seizures as well as by emotional and

cognitive dysfunctions. About 30% of epileptic patients are defined pharmacoresistant since they do

not adequately respond to therapies and in these patients, affected by TLE, the surgical removal of

the epileptic focus is often the only therapeutic option to achieve seizure control101

.

The evidences available from experimental and clinical findings support a crucial role of immune

and inflammatory processes in the aetiopathogenesis of epilepsy47

. Pronounced inflammatory

processes have been described in human epileptogenic brain tissue from TLE and epilepsies

associated with malformations of cortical development, where seizures are often refractory to

anticonvulsant treatments102

.

Pharmacological studies in experimental models and the use of transgenic mice with perturbed

cytokine systems showed that proinflammatory cytokines (e.g. IL-1 , TNF- ), danger signals (e.g.

HMGB1), complement factors and prostaglandins significantly contribute to seizure activity and

cell loss, and that inhibition of the production of these molecules or blockade of their receptors (e.g.

ILR1, TLR-4), significantly reduced seizure activity103

.

Recently, i-proteasomes have been detected in cortex and hippocampus of patients affected by

different TLE forms48

. By IHC staining i-proteasomes were revealed in glia and neurons of TLE

hippocampi whereas controls showed positivity to 1i and 5i only in luminal endothelial cells, as

30

observed in other studies49,50

. Intriguingly, the neuronal i-proteasome expression differs between

TLE forms. Furthermore, no IFN- was detected in TLE specimens suggesting that in this disease i-

proteasome synthesis is induced by a different mechanism. A good candidate could be TLR-4,

which can be triggered by LPS treatment, a well-known i-proteasome inducer. Thus, taking into

account the role that TLR-4 plays in the disease, we might speculate that i-proteasome is one of the

molecules induced/activated by TLR-4 pathway that mediate the effects that this receptor has

during epileptogenesis. Further investigations are however mandatory to address this issue as well

as to understand the role of i-proteasome in the different neuroinflammatory processes involved in

epilepsy.

It is worth to note that so far no studies have exploited the availability of i-proteasome ko mice by

crossing them with disease animal models (e.g. APP/PS1 model for AD) or inducing a disease-

associated symptoms by drugs such as pilocarpine or kainite (as epilepsy models), respectively. We

surmise that these types of studies will provide breakthrough information on the disease

aetiology/progression and the involvement of i-proteasome in the pathological mechanisms.

3.5 An overview of selective s- and i-proteasome inhibitors and

enhancers.

Despite the efforts made in recent years to discover selective inhibitors for either s-proteasome or i-

proteasome, only six compounds showing some selectivity (two for the s-proteasome, PR-893 and

PR-825, and four for the i-proteasome, PR-924, PR-957, IPSI-001 and UK-101) have been

described (Table 3.2 and Figure 3.2). PR-893 (compound 1 in Fig. 3.2) is a tripeptide epoxyketone

which shows a selectivity of 20 folds for β5 over β5i subunits104

. This molecule was used by Parlati

and co-workers to prove, in their pioneering study, the relationship between the selective inhibition

of subunits (β5 and β5i) with chymotrypsin-like (CT-like) activity and the antitumour response in

tumour cells of haematological origin. The same reactive portion of PR-893 can be found in PR-825

(compound 2 in Fig. 3.2), an analogue of carfilzomib (compound 2 in Fig. 3.1), which has been

synthesized by Zhou et al. by varying the P2, P3 and N-Cap of bortezomib105

. These variations gave

rise to an inhibitor of proteasomal CT-like activity, which is about 14 folds more selective towards

β5 as respect to β5i. On the other hand, two selective inhibitors related to carfilzomib (compound 2,

in Fig.3.1), namely PR-924 (comp. 3, Fig. 3.2) and PR-957 (comp. 4, Fig. 3.2), have been

developed to target β5i subunit of the i-proteasome106

. As in the case of the above-mentioned s-

proteasome inhibitors, these two compounds are tripeptide epoxyketones. Their structure differs

31

from s-proteasome selective inhibitors for the presence of three marked aromatic and hydrophobic

pharmacophoric features. In particular, both PR-924 and PR-957 have two aromatic moieties in the

proximity of the reactive group, i.e. epoxyketones phenylalanine instead of epoxyketone leucine of

PR-893 and PR-825 as well as bulkier tryptophan (PR-924) and tyrosine (PR-957) instead of

smaller side-chain amino acids for PR-893 and PR-825. Equally, the N-protecting groups are also

more hydrophobic and large as respect to s-proteasome inhibitors. All these hydrophobic/aromatic

features are expected to interact with specific moieties of the β5i binding site and, as discussed

below, these may constitute the molecular reason for the i-proteasome selectivity over s-proteasome

catalytic activity. In fact, PR-957 is reported to be 20- to 40- more selective for murine β5i over β5

and β1i subunits and, at the concentration which allows minimal impact on other subunits, it

inhibited the presentation of β5i-dependent epitopes31

. Equally, PR-924 was shown to be 100-fold

more selective for human β5i and less selective for CT-like activity of β5 as compared to

bortezomib and carfilzomib63

. The selective inhibition of i-proteasome has been obtained also with

a dipeptide aldehydes compound, IPSI-001 (compound 5, Fig. 3.2) that targets the β1i subunit and

inhibit i-proteasome C-like and CT-like activity in vitro in lymphoid-derived tumour cells lines and

MM patient-derived samples. Similarly to PR-924 and PR-957, IPSI-001 has three hydrophobic and

extended features such as the carboxybenzyl N-protecting group and the n-propyl side chain close

to the reactive aldehyde. Finally, another β1i specific inhibitor, UK-101, (compound 6, Fig. 3.2) has

been developed by Ho and co-workers, demonstrating growth-inhibitory activity in prostatic cancer

cells expressing higher level of β1i with no effect on s-proteasome function64,65

. UK-101, like IPSI-

001, is also a dipeptide with a tert-butyldimethylsilyl group attached at the C-terminal hydroxyl

group65

. All the above-mentioned s- and i-proteasome inhibitors covalently modify the catalytic N-

terminal Thr of the proteolytic β subunits thereby affecting specific activities by means of covalent

reversible or irreversible inhibition mechanisms (Table 3.3). Considering the large number of

promiscuous inhibitors of s- and i-proteasome, which can bind reversibly or irreversibly the

catalytic Thr, such as epoxyketones, aldehydes, vinylsulfones, β-lactones, boronic acids,

isocoumarins and Michael acceptors (Fig. 3.1 and Table 3.2), it should be pointed out that only

epoxyketones and aldehydes have been reported as i- and s-proteasome specific inhibitors

(compounds 1-6 in Fig. 3.2 and Table 3.3). In this regard, it should be noted that, while some efforts

have been done to modify the reactive threonine-trap of s-proteasome inhibitors107,108

, no similar

strategies have been yet explored to modify known specific i-proteasome scaffolds with other

reactive groups. It is therefore unclear whether other Thr-traps may be conveniently used for the

design of novel i-proteasome specific inhibitors. Similarly, it is interesting to note also that non-

32

peptide-like compound such as Salinosporamide (compound 11 in Fig. 3.1), Belactosin (compound

16-17 in Fig. 3.1) and Betulinic acid derivatives (compound 22 in Fig. 3.1) have been so far only

described among promiscuous s- and i-proteasome inhibitors (Fig. 3.1). Even in this case, it is still

to be ascertained whether it is possible to optimize classes of non-peptide-like compounds in order

to reach some selectivity either for s- or i-proteasome. In this context, new insights on

immunoproteasome structure obtained either by modelling results (see next paragraph) or new

crystallographic resolutions promise to be highly relevant for the rational design of novel selective

inhibitors. Although the inhibition mechanism of proteasomes has been extensively reviewed by

several authors106,109–112

, few information about mechanisms of activation or reversible inhibition

are available. For example, betulinic acid derivatives (compound 22 in Fig. 3.1) were proved to

inhibit CT-like activity113

but there is no specific information about their inhibition mechanisms, as

well as for Argyirn A (compound 24 in Fig. 3.1)114

. On the other hand, Gallastegui et al.

demonstrated, by resolving the crystal structure of s-proteasome in complex with the most potent

hydroxyurea derivative (PDB ID: 3SHJ)115

, that new hydroxyurea derivatives (compound 28 in Fig.

3.1) bind non-covalently the site with CT-like activity. In contrast to the development of 20S

inhibitors, drug-like molecules that can activate or enhance proteasome activity are, at present, rare

and not well characterized, even if they could represent an appealing strategy for specific diseases

in which proteasome activity has to be increased to cure the patient. Apart the role of PA28, PA700

and PA200 regulators in proteasome activation, several type of small molecules including SDS,

lipids (oleic and linoleic acids) and peptide–based compounds were shown to activate proteasome at

relatively high concentrations116

. On the contrary, oleuropein (compound 8, Fig. 3.2),

Phaeodactylum tricornutum algae extract and betulinic acid (compound 7, Fig. 3.2) can activate

proteasome at low micromolar concentration. Oleuropein is the major component isolated from the

Olea europaea, it is able to increase in vitro all three proteolytic activities and delay replicative

senescence of human embryonic fibroblast117

. The same results have been obtained by

Phaeodactylum tricornutum algae extract, which stimulates all three proteolytic activities of

proteasome in vitro and in human keratinocytes thereby reducing the level of oxidized proteins118

.

Conversely, betulinic acid, a triterpene derived from many plant species, preferentially activates the

CT-like activity of proteasome119

, even if inhibitory effects have also been reported as above

described. Finally, Chondrogianni and co-workers identified quercetin (compound 9, Fig. 3.2) and

its derivative, namely quercetin caprylate (QU-CAP) as a proteasome activator with anti-oxidant

properties that can influence cellular lifespan, survival and viability of HFL-1 primary human

fibroblasts. Moreover, when these compounds were supplemented to already senescent fibroblasts,

33

a rejuvenating effect was observed120

. The mechanism of action of all these compounds is supposed

to be linked to conformational changes of the channel gates regulated by the subunits of the

proteasome although further investigations are needed to characterize their enhancer properties. In

addition, recent and ongoing research aim to elucidate the roles of other components of the UPS has

identified several enzymes, beside the 20S catalytic core, that can be additional targets for

therapeutic intervention by small-molecule modulators121

. In particular, an enhancement of

proteasome activity by a small-molecule inhibitor of the DUB USP14 has been reported122

, as well

as an anticancer activity of another compound which inhibits both UCHL5 and USP14 enzymes123

,

suggesting that the deubiquitinating activity of the PA700 regulator could represent a new

anticancer drug targets.

3.6 Human immunoproteasome model.

Despite the importance of i-proteasome as an emerging biological target for cancer and

neuropathologies, until 2013 no crystallographic structure of the human form has been solved. A

possible strategy to compensate that lack of knowledge was the generation of in silico human i-

proteasome models, which could provide helpful hints for the development of selective i-

proteasome modulators. Accordingly, we generated the human model of the whole i-proteasome by

starting from the X-ray structure of the mammalian 20S proteasome (PDB id: 1IRU) and

substituting of the six catalytic subunits of the inner rings with the related i-proteasome subunits

(Table 3.4).

At first we checked the similarity between subunits in the two inner rings of the proteasome

structure. Root mean square deviation (RMSD) for each couple of homolog-subunits was found to

be in the range 0.6-1.3 on all atoms, thereby indicating that homolog- -subunits of the two inner

rings can be considered equivalent. The raw models of i-subunits were taken from the

SwissModel repository124

and aligned to chain H, I, L for 1, 2 and 5, respectively. The same

models were used also to represent the i-proteasome subunits of the other inner ring. The entire i-

proteasome model was generated by substitution of the six catalytic chains and followed by several

structural corrections/refinements such as hydrogen addition, water and ions removal, manual

charges corrections, addition of missing side chains and optimization of hydrogen bonds. In order to

analyze at the molecular level the aminoacidic differences between s- and i-proteasome we reported

in Fig. 3.3 and Table 5.3 a pair-wise comparison of the subunit catalytic pockets of the

34

mammalian s-proteasome crystallographic structure (PDB id: 1IRU) and the model of human i-

proteasome that we derived.

Figure 3.2. Chemical structures of selective s- and i-proteasome inhibitors and activators of proteasomes.

35

Compound

Selectivity Mechanism and

biological effect Subunit Activity

1 β5 CT-like irreversible inhibitor

2 β5 CT-like irreversible inhibitor

3 β5i CT-like irreversible inhibitor


5 β1i

CT-like > C-like > T-

like

BrAAP

reversible inhibitor


7 αa activator

8 αa activator

9 αa activator

Table 3.3. Activity profiles of s- and i-proteasome inhibitors as well as proteasome enhancers. Inhibition form,

inhibited proteasome activities and relative references are reported. Proteasome activities, as defined with short-

fluorogenic substrate assay, are shortened as following: CT-like = chymotrypsin-like, T-like = trypsin-like, C-like =

caspase-like. Structure enumeration of proteasome inhibitors refers to what reported in Fig. 3.2. αa: the mechanism of

these compounds has been hypothesised to be related to conformational change perturbations of α-type subunits.

36

Figure 3.3. Comparison of s-proteasome and i-proteasome β-subunits in complex with bortezomib. S-proteasome

subunits were taken from the crystallographic structure of the human 20S proteasome (PDB id: 1IRU) while i-

proteasome subunits were derived by our model (see above). Initial binding poses of bortezomib (compound 1 in Fig.

1) were taken by the crystal structure of the yeast proteasome (PDB id: 2F16) aligned with correspondent subunits of

the human proteasome (PDB id: 1IRU) and followed by ligand minimizations. Contour maps were generated with the

software SiteMap [136], thereby producing hydrophobic (yellow regions), donor (blue regions) and acceptor (red

regions) potentials. Contour maps represent the ideal region of the space where a corresponding ligand feature should

be located in order to interact optimally with the subunits.

37

The catalytic binding sites of s- and i-proteasome generally show a similarity in shape and volume

between β1, β2 and β5 and their corresponding β1i, β2i and β5i subunits. This simple fact might

suggest that ligands with similar shape and volume properties fit equally in s- and i-proteasome

homologue subunits. However, a deeper structural analysis shows that non-negligible differences

between s-proteasome and i-proteasome are present in the aminoacidic composition of some of the

catalytic pocket residues. In particular, we analyzed residues of the binding site that differ from s- to

i-proteasome chains observing that β1 and β1i subunits show the higher degree of variability since

ten residues differ in the substrate binding site (Fig. 3.3 and Table 5.3). On the other hand, five

residues differ from β5 and β5i subunits whereas only four residues change from β2 to β2i subunits.

Most interesting is to analyze the differential nature and position of these changes vis-à-vis to the

binding of putative s- or i-proteasome inhibitors. We did such analysis by taking advantage of the

structural information available from co-crystallized structure of bortezomib with yeast s-

proteasome (PDB id: 2F16). In Fig. 3.3 we graphically reported the location of the aminoacids

listed in Table 5.3 and depicted binding site differences in terms of contour maps that represent the

ideal region of the space where a ligand feature should be located in order to interact optimally with

the single subunits. In some cases differences involve relevant changes of aminoacid properties that

are likely to be important for the binding of putative selective inhibitors. For instance Arg45 of the

β1 subunit is substituted to a Leu45 in the β1i subunit. This change implies the existence of a

noticeable hydrophobic region in the binding site of the β1i subunit (yellow region, Fig. 3.3) that is

not present in the β1 subunit due to the polar nature of the arginine residue. Despite such a

difference occurs in the binding site of both subunits, bortezomib seems not to be influenced by this

change and this might constitute one of the reasons behind the promiscuous nature of this inhibitor

towards s- and i-proteasome. Another major difference involves residues of Ala27 and Ser28 of the

β5 subunit, which are inversed in the β5i subunit, i.e. Ser27 and Ala28. This simple inversion

relocates the hydrophilicity of the serine residue thereby becoming more accessible for the putative

binding of ligands. Even in this case the non-selectivity of bortezomib may be explained by the fact

that its binding is not influenced by such aminoacidic switch, although selective lead compounds

might be developed in light of these considerations. In contrast, β2 and β2i subunits seem to be very

similar in terms of aminoacidic properties. This fact is graphically reflected by similar shapes and

colours of the contour maps of Fig. 3.3. Thus, from these data, it appears particularly challenging to

exploit differential binding site composition of β2 and β2i subunits in order to conceive selective

inhibitors. This consideration may also explain why no selective s- or i-proteasome inhibitors

targeting the trypsin-like (T-like) activity have been discovered so far. Finally, by comparing β1, β2

38

and β5 subunits it is interesting to note that both in the crystal structure of the yeast proteasome

(PDB id: 2F16) as well as in our i-proteasome model, bortezomib assumes a markedly different

molecular conformation in β2 and β2i while other subunits, i.e. β1/β1i and β5/β5i seem to

accommodate the ligand with the same binding mode (Fig. 3.3). Huber et al.125

published several

crystal structures of the yeast 20S proteasome and of the mouse 20S s-proteasome and i-proteasome

in presence or absence of the i-proteasome specific inhibitor PR-957 (compound 4 in Fig. 3.2; PDB

ids: 3UN4, 3UN8, 3UNB, 3UNE, 3UNH and 3UNF). Through the analysis of the crystallographic

structure the authors identified a unique catalytic feature for the i-proteasome β5i active site and,

together with conformational changes occurring upon ligand binding, could rationalize the

selectivity of PR-957 towards the β5i subunit. Importantly, the superposition of the above-described

crystallographic subunits β1i, β2i and β5i with those obtained by our human i-proteasome model

shows a striking consistency, thereby underlining the importance to obtain structural and/or

modelling data as an effective tool for the identification of potential small-molecule lead structure,

as described below.

3.7 Computer-aided drug design approaches

Few approaches of computational drug design have been applied in the last years for the discovery

of new lead compounds able to inhibit s-proteasome126–128

. For instance, Reboud-Ravaux and

colleagues carried out a multistep structure-based virtual ligand screening strategy and were able to

identify several novel lead compounds inhibiting s-proteasome with micromolar range activity and

reported cytotoxicity on human tumour cell lines127,129

. It is worth noting that no computer-aided

drug design techniques have been yet reported for the discovery of selective i-proteasome

inhibitors. In this context, likely, new insights on i-proteasome structure obtained by computational

modelling128

, including the present work, or by means of new crystallographic evidences125

, might

constitute a new way to deploy chemoinformatic techniques such as docking or pharmacophore

high-throughput virtual screenings130,131

on large database of chemicals compounds132,133

. In

particular, it appears that differences of aminoacids (Fig. 3.3 and Table 5.3) in the s- and i-

proteasome β subunit binding sites (especially β1 versus β1i and β5 versus β5i) might effectively

constitute the molecular basis for the development of novel s- and i-proteasome specific inhibitors,

keeping however into account that the design of highly-specific inhibitors may still result in

challenging tasks because of the major similarities in the β subunit catalytic sites.

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Proteasome

subunit – gene

name

Common

Name Uniprot code

Chain name

(PDB id:

1IRU)

Immuno-

proteasome

subunit – gene

name

Common

Name Uniprot code

PSMB6 1 P28072 H/V PSMB9 1i P28065

PSMB7 2 Q99436 I/W PSMB10 2i P40306

PSMB5 5 P28074 L/Z PSMB8 5i P28062

Table 3.4. S- and i-proteasome catalytic subunit nomenclature.

Subunits Proximal residues (within 5Å from bortezomib pose)

20 21 22 23 27 28 31 45 46 48 52 95 97 116

β1 T T T A T R T M G M

β1i V S A V F L A S H G

β2 E G T T

β2i N D V A

β5 T A S A G

β5i S S A S C

Table 3.5. Proximal amino acidic differences in the catalytic binding sites of s- and i-proteasome β-subunits.

3.8 Hit Identification

Since the growing amount literature data suggesting the pivotal role of i-proteasome in different

cellular pathways, including apoptosis and inflammation, a selective modulation of i-proteasome,

by inhibiting or enhancing its activity, directly with small-molecule modulators or indirectly, e.g. by

regulating E3 and DUB enzymes, could represent a promising strategy to counteract these

pathologies. At the time of this study, medicinal chemistry efforts identified a small number of low

molecular-weight inhibitors that are able to modify 20S s- and i-proteasome functions, but only few

examples of selective i-proteasome inhibitors was developed. So we started an hit identification

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campaign to find new selective i-proteasome inhibitors using virtual screening methodologies. The

available x-ray structure of human proteasome (PDB code 1IRU) and the generated model of i-

proteasome, after a standard protein preparation procedure, were used as template to virtually

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